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protein solution is then concentrated five-fold and diafiltered 2× (in P-21/DF-101) using WFI as diluant. This step takes approximately 5 h and requires a membrane of 20 m 2. The product yield is 97%. The concentrated protein solution is then chemically treated for 1.5 h with Polysorbate 80 to inactivate viruses (in P-22/V-111). An Ion Exchange chromatography step follows (P-24/C-102). The following operating assumptions were made for this step: (1) the resin’s binding capacity is 40 g of product per L of resin; (2) a gradient elution step is used with a sodium chloride concentration ranging from 0.0 to 0.1 M and a volume of 5 CVs; (3) the product is recovered in 2 CVs of eluant buffer with a yield on MAb of 90%; and (4) the total volume of the solutions for column equilibration, wash, regeneration, and rinse is 16 CVs. The step takes approximately 22 h and requires a resin volume of 211 L. Ammonium sulfate is then added to the IEX eluate (in P-25/V-109) to a concentration of 0.75 M to increase the ionic strength in preparation for the Hydrophobic Interaction Chromatography (P-26/C-103) that follows. The following operating assumptions were made for the hydrophobic interaction chromatography (HIC) step: (1) the resin binding capacity is 40 g of product per L of resin; (2) the eluant is a Sodium Chloride (4% w/w) Sodium Di-hydro Phosphate (0.3% w/w) solution and its volume is equal to 5 CVs; (3) the product is recovered in 2 CVs of eluant buffer with a recovery yield of 90%; and (4) the total volume of the solution for column equilibration, wash and regeneration is 12 CVs. The step takes approximately 22 h and requires a resin volume of 190 L. A viral exclusion step (DE-105) follows. It is a dead-end type of filter with a pore size of 0.02 μm. This step takes approximately 2.8 h. Finally the HIC elution buffer is exchanged for the phosphate buffered saline (PBS) solution and concentrated 1.5-fold (in DF-102). This step takes approximately 8 h and requires a membrane of roughly 10 m^2. The 774 L of final protein solution is stored in twenty 50 L disposable storage bags (DCS-101). Approximately 19.5 kg of MAb are produced per batch. The overall yield of the downstream operations is 64.4%. After the process specifications have been completed and the process model has been simulated, the full results may be viewed and analyzed. Results include material input and output compositions and amounts, equipment size calculations, process scheduling information, process costing, etc. Partial results for this example process are described in the following sections.
2.3. Material Balances
Table 1 provides a summary of the materials used by the process, with raw material requirements listed per year, per batch, and per kg of Main Product (MP). These results were calculated by the process simulator, based upon the input parameters specified for relevant operations such as material charges. Note the large amount of WFI utilized per batch. The majority of WFI is consumed for cleaning and buffer preparation. In addition to calculating the overall raw material requirements, process simulators calculate the amounts and compositions of each individual stream (inputs, intermediates and outputs). This provides useful information for verifying results related to material transformations and separations, liquid and solid waste generation, emissions, equipment capacity requirements, etc.
Table 1. Raw Material Requirements (MP = purified MAb). Material kg/yr kg/batch kg/kg MP Inoc Media Sltn 4888 232.76 11. WFI 1,072,146 51,054.58 2617. SerumFree Media 9419 448.52 23. H3PO4 (5% w/w) 242,994 11,571.15 593. NaOH (0.5 M) 226,036 10,763.64 551. Air 867,389 41,304.23 2117. Protein A Equil 446,384 21,256.40 1089. Protein A eluti 202,216 9629.33 493. Prot-A Reg Buff 121,398 5780.86 296. NaOH (0.1M) 135,799 6466.60 331. IEX-Eq-Buff 66,503 3166.82 162. IEX-Wash-Buff 66,797 3180.80 163. IEX-El-Buff 3765 179.29 9. NaCI (1 M) 40,835 1944.51 99. Amm. Sulfate 2845 135.46 6. HIC-Eq-Buff 26,371 1255.75 64. HIC-Wash-Buff 62,681 2984.82 153. HIC-El-Buff 60,792 2894.86 148. NaOH (1 M) 65,709 3129.00 160. PBS 32,483 1546.83 79. Polysorbate 80 2 0.08 0. TOTAL 3,757,452 178,926.28 9173.
2.4. Scheduling and Cycle Time Reduction
As noted previously, a unique feature of batch process simulators (as opposed to continuous simulators) is their ability to model the time-dependent aspects of batch processes. This enables the automatic generation of a process schedule. Figure 3 displays the schedule for four consecutive batches of this example process. The equipment units are shown on the vertical axis while the time is shown on the horizontal axis. The four batches are represented with four different colors. This figure shows the equipment occupancy for a plant that has a single production train. The cleaning-in-place (CIP) skids can be seen at the top of the figure. The other main equipment can be seen further down. Note that Figure 3 does not show all equipment required for this process; various filters, mixing tanks, and other minor equipment have been excluded from the chart. The batch time for this process is approximately 50 days. This is the time required from the start of inoculum preparation to the final product purification of a single batch. The cycle time—the time between consecutive batch starts—is determined by the cycle time bottleneck, which is the production bioreactor (BR-101) in this case. The minimum cycle time is just under 14 days and, for this example, has been rounded to exactly 14 days. Based on the batch time, the batch cycle time, and an assumed plant uptime of 330 days/year, this plant can complete roughly 21 batches per year, producing approximately 410 kg/year of purified MAb. It is clear from Figure 3 that under these conditions the downstream purification train is under-utilized
Figure 4. Four bioreactor trains feeding one purification train.
2.5. Economic Evaluation
Accurate project cost analysis and economic evaluation are critical during late-stage development and commercialization of a product. For new products, if a company lacks a suitable manufacturing facility with sufficient available capacity, it must decide whether to build a new plant, retrofit an existing plant, or outsource the production. Building a new plant is a major capital expenditure and a very lengthy process. In order to make a well-informed decision on whether to build (or retrofit) a plant, management must have information on the capital investment required and the timeline to complete the facility. If a company chooses to outsource production instead, a cost-of-goods analysis provided by a process model can serve as the basis for discussion of the process and negotiation with contract manufacturers. Contract manufacturers usually base their offerings on requirements of facility/equipment utilization and labor per batch, which may be provided by the model. The data from the preceding analysis can be leveraged to perform a financial evaluation of the process. To accomplish this, the cost of equipment is first estimated by the software using built-in cost correlations that are based on data derived from a number of vendors and literature sources. The fixed capital investment is then estimated based on equipment costs and various multipliers. Some of these multipliers are equipment-specific (e.g., installation cost) while others are process-specific (e.g., cost of piping, buildings, etc .). This approach is described in detail in the literature [12–14]. The rest of this section provides a summary of SuperPro Designer’s cost analysis results for this example process.
Table 2. Major Equipment Specification and Purchase Costs (Year 2013 prices in US $).
Quantity Name Description Unit Cost ($) Total Cost ($) 4 BBS-101 Rocking Bioreactor Skid 548,000 2,192, Container Volume = 100 L 6 BBS-102 Rocking Bioreactor Skid 548,000 3,288, Container Volume = 200 L 1 MP-101 Blending Tank 186,000 186, Vessel Volume = 800 L 3 SBR-101 Seed Bioreactor 1,201,000 3,603, Vessel Volume = 1200 L 1 MP-102 Blending Tank 226,000 226, Vessel Volume = 3200 L 3 SBR-102 Seed Bioreactor 1,351,000 4,053, Vessel Volume = 4800 L 1 MP-103 Blending Tank 257,000 257, Vessel Volume = 11,000 L 4 MP-104 Blending Tank 197,000 788, Vessel Volume = 1200 L 4 BR-101 Bioreactor 1,948,000 7,792, Vessel Volume = 19,000 L 1 V-101 Blending Tank 280,000 280, Vessel Volume = 17,000 L 1 DS-101 Disk-Stack Centrifuge 469,000 469, Throughput = 2000 L/h 1 V-103 Blending Tank 276,000 276, Vessel Volume = 16,000 L 1 C-101 PBA Chromatography Column 622,000 622, Column Volume = 480 L 1 V-107 Blending Tank 236,000 236, Vessel Volume = 4300 L 1 DF-101 Diafilter 65,000 65, Membrane Area = 20 m^2 1 V-111 Blending Tank 188,000 188, Vessel Volume = 870 L 1 C-102 PBA Chromatography Column 451,000 451, Column Volume = 210 L 1 V-109 Blending Tank 204,000 204, Vessel Volume = 1500 L 1 C-103 PBA Chromatography Column 439,000 439, Column Volume = 190 L 1 V-108 Blending Tank 199,000 199, Vessel Volume = 1300 L 1 V-110 Blending Tank 199,000 199, Vessel Volume = 1300 L 1 DF-102 Diafilter 42,000 42, Membrane Area = 10 m^2 Unlisted Equipment 9,661, GRAND TOTAL 35,716,
Table 6. Raw Materials Cost Breakdown (Year 2013 prices in US $).
Bulk Material Unit Cost($) Annual Amount(kg) Annual Cost($) % Inoculation Media 6.15 18,854 115,893 0. WFI 0.15 4,135,421 620,313 4. SerumFree Media 300.00 36,330 10,899,050 82. H3PO4 (5% w/w) 0.14 937,263 133,560 1. NaOH (0.5 M) 0.25 871,855 213,657 1. Protein A Equil 0.15 1,721,769 263,344 1. Protein A eluti 0.15 779,976 119,711 0. Prot-A Reg Buff 0.17 468,249 78,539 0. NaOH (0.1M) 0.24 523,795 127,345 0. IEX-Eq-Buff 0.19 256,512 47,746 0. IEX-Wash-Buff 0.22 257,645 56,884 0. IEX-El-Buff 0.35 14,522 5045 0. NaCI (1 M) 0.37 157,505 58,008 0. Amm. Sulfate 8.00 10,972 87,775 0. HIC-Eq-Buff 0.91 101,716 92,438 0. HIC-Wash-Buff 0.54 241,771 130,073 0. HIC-El-Buff 0.31 234,484 71,432 0. NaOH (1 M) 0.34 253,449 85,220 0. PBS 0.18 125,293 22,839 0. Polysorbate 80 1.83 6 12 0. TOTAL 13,228,884 100.
Table 2 provides a list of the major equipment items in this project, along with their purchase costs. The total equipment cost for a plant of this capacity (four production bioreactors each having a working volume of 15,000 L and a total volume of around 19,000 L) is approximately $36 million. Almost one quarter of the equipment cost is associated with the four production bioreactors. The cost of filters and inoculum preparation items that are seen in Figure 2 but are missing from the table are accounted for under the “Unlisted Equipment” item near the bottom of this table. This economic evaluation also takes into account the vessels required for buffer preparation and holding that are not included in Figure 2. A full model that includes all buffer preparation and holding activities and other advanced process modeling features can be downloaded from www.intelligen.com/demo. Table 3 displays the various items included in the direct fixed capital (DFC) investment. The total DFC for a plant of this capacity is around $392 million, or approximately 11 times the total equipment cost. The total capital investment, which includes the cost of start-up and validation, is around $512 million. Table 4 provides a summary of the operating cost for this project. The total annual operating cost is $134 million, resulting in a unit production cost of around $84.7/g (1,580 kg of purified product are produced annually). The facility-dependent cost is the most important item, accounting for roughly half of the overall operating cost. This is common for high value biopharmaceuticals. Depreciation of the fixed capital investment and maintenance of the facility are the main contributors to this cost.
Consumables and Labor are the second and third largest operating costs, at 16% and 14% of the total, respectively. Consumables include the cost of chromatography resins and membrane filters that need to be replaced on a regular basis. The replacement of the Protein-A resin accounts for 73% of the total consumables cost (see Table 5). A unit cost of $6000/L and a replacement frequency of 60 cycles were assumed for the Protein-A resin. Raw materials account for around 10% of the overall cost. The main raw material cost contributor is Serum Free Media, which accounts for 82% of the raw materials cost (see Table 6). This is based on an assumed price of $300/kg for Serum Free Media in dry powder form. Approximately 72% of the manufacturing cost is associated with the upstream section (inoculum preparation and fermentation) and 28% with the downstream section (product recovery and purification). Additional details related to bioprocess design and economics can be found at www.intelligen.com/ literature (reference #2 on this webpage).
2.6. Sensitivity Analysis
After a model of the proposed process has been developed on the computer, tools like SuperPro Designer can be used to ask and readily answer “what if” questions and to carry out sensitivity analysis with respect to key design variables. For example, there may be uncertainty regarding the annual demand for the final product, the product yield during fermentation at full-scale production, the recovery in the downstream purification units, etc. These factors may have a large impact on the overall economics of a process. Therefore it is important to understand the effect of changing these types of variables in order to determine whether or not it is wise to move forward with a project. In this example, sensitivity analyses were performed to understand the impact on unit production cost of the number of bioreactor trains, the product titer, and the bioreactor volume. Figure 5 displays the impact of bioreactor trains on the unit production cost. Note that the cost analysis calculations in Section 2.5 correspond to the case of four production bioreactors (each having a working volume of 15,000 L) feeding a single purification train and resulting in a unit cost of around $85/g. In contrast, if just a single bioreactor train feeds the purification train, the manufacturing cost increases by over 80%. Furthermore, as production bioreactors are added to the plant (while keeping a single purification train), the unit cost drops slowly and asymptotically approaches a value of around $80/g. Multiple production bioreactors that feed a single purification train lead to reduced manufacturing cost because the plant throughput is increased (since it is proportional to the number of bioreactors) without the need for additional capital investments in the purification train. A ratio of four or five bioreactor trains per purification train is probably the optimum number for cell culture processes that have a fermentation time of around twelve days. Such processes typically operate with cycle times ranging between 3.5 and 2.5 days. Figure 6 displays the impact of product titer and bioreactor volume on the unit production cost. All points correspond to four production bioreactors feeding a single purification train. For low product titers, the bioreactor volume has a considerable effect on the unit production cost. For instance, for a bioreactor product titer of 1 g/L, going from 10,000 L to 20,000 L of production bioreactor volume reduces the unit cost by about $64/g (from approximately $194/g to $130/g). On the other hand, for high product titers (e.g., around 2.5 g/L), the impact of bioreactor scale is not as important (the differential is only about $27/g). This is because at high product titers, a higher percentage of the
resource capacity and time utilization, equipment sizing information, capital and operating cost results, etc. Such models facilitate scale up/down calculations, cycle time analysis, economic evaluation, technology transfer, and process fitting. The primary objective of such models is to optimize processes under development. In contrast, modeling of multiproduct batch plants is focused on evaluating the interactions among multiple processes running concurrently and/or sequentially in a plant. This is important since most biotech facilities are multiproduct plants. Furthermore, in most applications of multiproduct plant modeling, the process flows and equipment sizes are quite well defined. As a result, such models place less emphasis on process design calculations and more emphasis on timing and utilization of shared resources such as equipment, utilities, labor, and inventories of materials. The sharing of resources across multiple processes renders the design and operation of multiproduct facilities more challenging than design and operation of a single isolated process, and computer models developed for such environments must capture the interaction among production lines at the facility level. In other words, during the design of multiproduct facilities, computer models must be able to determine the overall resource requirements for many different processes which will be run simultaneously. After the facility is built, models may be used to determine the manufacturing capacity for different production scenarios. They are also used to generate feasible production schedules that respect all major production constraints. Production scheduling results are typically communicated through Gantt charts and reports that provide information on tasks that need to be executed during a certain time period. Furthermore, due to the inherent variability of biological processes, scheduling tools employed in the biopharmaceutical industry must be able to efficiently handle conflict resolution and rescheduling.
3.1. Applications of Multiproduct Plant Modeling
Typical applications of multiproduct plant modeling include: Capacity Analysis and Strategic Planning Production Scheduling Facility Design and Debottlenecking Each of these applications is explained in greater detail below: Capacity Analysis and Strategic Planning— Capacity analysis models provide a high-level estimate of the manufacturing capabilities of existing or planned plants, based on the availability of key resources such as production lines. The main objectives may include determining the production rate for existing facilities or determining which of several facilities is best for a new product. These types of models may also be used to estimate the demand for raw materials that need to be purchased, especially those requiring long lead times. Capacity analysis models tend to rely on simplified recipes since they need to cover the production of many different products over a period of a year or longer.
Production Scheduling— The objective of this activity is to assign specific resources to each production campaign, estimate the start times for campaigns and batches, and determine the time horizon for producing specific quantities of a particular combination of products in order to meet market demands. The time horizon is usually weeks to months but it may extend to a year for certain industries such as biopharmaceuticals.
Facility Design and Debottlenecking— This is the inverse of the capacity analysis problem. During the design of new facilities or the retrofit of existing ones, multiproduct plant models are used to estimate resource levels required to achieve a certain production volume within a given time period. Such models should account for the occupancy of all equipment and resources whose supply may be limited. Apart from raw materials, typical resources include main equipment (e.g., reactors, filters, dryers, etc .), auxiliary equipment (e.g., shared pipe segments, transfer panels, cleaning equipment), utilities (e.g., steam, purified water, and electrical power), and various types of labor. Engineering companies involved in the design of new facilities frequently use these types of models [3]. Debottlenecking activities are aimed at increasing the production of existing manufacturing lines and facilities, and they may result in improvements to production scheduling and/or retrofitting of facilities in order to increase production capacity.
The above applications require plant models with increasing levels of detail. Capacity analysis models require the least detail because they do not need to capture specific details about how to execute the processes. Production can be represented with simplified campaigns associated with key equipment items (or manufacturing lines) that are likely to limit throughput. Long term raw material planning utilizes models with a level of detail similar to those of capacity analysis. In contrast, production scheduling models need to communicate when to execute process operations. Therefore they require greater detail than capacity analysis models in order to account for the utilization of all main equipment items as well as critical auxiliary equipment and other key resources ( i.e ., ones with high utilization that are likely bottlenecks). Finally, the most detailed models are required for cycle time reduction and debottlenecking studies, which must account for the occupancy of all equipment (main and auxiliary) as well as all other resources (e.g., utilities and materials) whose supply may be limited at any point in time. Although highly-detailed models such as the ones used for design and debottlenecking studies could theoretically be used for short term production scheduling, it is advisable to keep models as simple as possible so that the problem remains manageable.
3.2. Approaches to Modeling of Multiproduct Batch Plants
The approaches and tools utilized for modeling and scheduling of multiproduct batch plants vary widely, depending on the specific application and the sophistication of the user. A general categorization of these tools is listed here:
Spreadsheet Tools Batch Process Simulation Tools Discrete Event Simulation Tools Mathematical Optimization Tools Recipe-Based Scheduling Tools Typical uses of these tools for multiproduct plant modeling are described below: Spreadsheet Tools— Plant scheduling staff manually color spreadsheet cells to represent the equivalent of equipment occupancy charts for consecutive batches. Some users have implemented scripts that color cells based on batch recipe descriptions as well. However, this approach is time consuming, cannot be very detailed, and cannot be readily updated to account for delays and equipment
